IMPORTANT NOTE:

All Manylabs 2 materials (data, output, scipts, etc.) are in the ManyLabs2 repository https://github.com/ManyLabsOpenScience/ManyLabs2 or on the OSF page: https://osf.io/ux3eh/

The manylabRs package currently does not function as welll as we would like, so currently we advise to use the sourceable file in the ManyLabs 2 repository, you might also need package invctr.

library(devtools)
devtools::source_url("https://raw.githubusercontent.com/ManyLabsOpenScience/manylabRs/master/R/manylabRs_SOURCE.R")

# NOTE: Don't run init() if you do not want unsupervised loading, and possibly installing of packages we need to run ML2 scripts!!
# init()

devtools::source_url("https://raw.githubusercontent.com/FredHasselman/invctr/master/R/invictor.R")

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Install the package

Several ways to install the package.

Source from GitHub

Use the code below to install the manylabRs package directly from GitHub.

library(devtools)
install_github("ManyLabsOpenScience/manylabRs")

Download tarball from GitHub

First download the tarball, then install the package locally through the RStudio package installer: Tools >> Install Packages...

Main function

The main function to inspect is get.analyses().

It will take one or more take analysis (studies) from the masteRkey sheet and an indication of whether the analysis is:

1. global - will disregard the clusters in the data and use all valid caes for analyses, both primary and secondary analyses have a global variant.
2. primary- target analysis of replication study conducted for each lab seperately.
3. secondary - additional analyses conducted for each lab seperately.
4. order - presentation order analyses disregard the clusters int he data, each order is analysed seperately

Have a look at saveConsole.R which calls the testScript() function and creates a log file with lots of info about the analysis steps.

The example below runs a global analysis for Huang.1

library(manylabRs)
library(tidyverse)

df <- get.analyses(studies = 1, analysis.type = 1)

The object df contains two named lists:^[these names correspond to the analysis name in the masteRkey spreadsheet]

raw.case

This list contains dataframes with the relevant variables for each analysis, but before the analysis specific variable functions (varfun) are applied. There is a Boolean variable case.include which indicates whther a case is valid and should be included for analysis.

df$raw.case$Huang.1

aggregated

The dataframe in aggregated contains the data as is was analysed, after the varfun is applied.

df$aggregated$Huang.1

Other algorithms

• Get information from masteRkey on the analyses to run: get.info()
• Get a data filter based on exclusion criteria: get.chain()
• Select the appropriate variables: get.sourceData()
• Apply the analysis-specific variable function: varfun.ABC.#()
• Apply the analysis listed in column stat.test to the data
• Organise the output get.desriptives()
• Calculate confidence intervals for effect sizes any2any()
• Return the ouput

Other algorithms

• Get information from masteRkey on the analyses to run: get.info()
• Get a data filter based on exclusion criteria: get.chain()
• Select the appropriate variables: get.sourceData()
• Apply the analysis-specific variable function: varfun.ABC.#()
• Apply the analysis listed in column stat.test to the data
• Organise the output get.desriptives()
• Calculate confidence intervals for effect sizes any2any()
• Return the ouput